Cardiometabolic disease - a clustering of medical conditions and risk factors which includes obesity, diabetes, impaired liver function, and an increased risk in children for adult-onset cardiovascular disease - represents a major population-wide health burden in the United States. Management of cardiometabolic disease also imposes a substantial financial burden on the economy and ties up significant healthcare resources. It is well-known that many of the lifestyle and health behaviors that contribute to cardiometabolic disease are difficult to modify once established, and childhood represents an opportune time for promoting healthy behaviors. Patient-centered outcomes research (PCOR) has identified certain health behaviors as important and actionable in modifying cardiometabolic risk, namely weight management, physical activity, screen-time, sleep, and consumption of sugar-sweetened beverages. Mobile health technology (mHealth) could be used to monitor and counsel on common health behaviors associated with cardiometabolic risk, which may facilitate the inclusion of PCOR evidence on cardiometabolic disease into clinical practice. The overall goal of this research is to use mHealth technology to accelerate the uptake of PCOR findings on treatment of cardiometabolic disease. To achieve our goal, this study will develop a novel set of mHealth tools capable of collecting health behavior information and determine to what extent providing clinical feedback on these health behaviors improves obesity and health behaviors among children ages 2-12 year and their families. In this study we will develop, implement, and test the comparative clinical effectiveness of a closed- loop feedback system for collecting patient data and providing recommendations. The specific aims of this study are: 1) to develop an integrated closed-loop feedback system that incorporates longitudinal mHealth data in managing cardiometabolic disease among at-risk families, and 2) to determine the extent to which an integrated closed-loop system that provides feedback on objective patient-generated data improves cardiometabolic risk, as measured by changes in body mass index and health behaviors including, physical activity, screen-time, sleep, and sugar-sweetened beverage consumption. This research will develop novel mHealth tools and approaches that will allow healthcare providers and patients to better understand disease risk and improve disease management by collecting patient data 1) repeatedly over time, 2) simultaneously, and 3) objectively. This study is innovative because it will use mHealth tools to simultaneously collect longitudinal data on multiple health behaviors known to be associated with cardiometabolic risk, and it will offer a new approach to implementing and disseminating PCOR findings via a novel closed-loop feedback system. The high prevalence of cardiometabolic disease makes this innovative closed-loop system very relevant to public health. The mHealth tools and methods developed by this study for collecting patient data and providing clinical feedback could also easily be adapted and applied to a range of other health conditions.